By Prof. Xiangliang Zhang (CEMSE KAUST)
In this “big-data" era, vast amount of continuously arriving data can be found in various fields, such as sensor networks, network management, web and financial applications. To process such data, algorithms are usually challenged by its complex structure and high volume. Representation learning facilitates the data operation by providing a condensed description of patterns underlying the data. In this talk, representation learning models will be discussed for profiling users from their movement trajectories, for characterizing the dynamic density of the data stream by an online density estimator, and for approximating and compacting data stream by a number of consecutive line segments. Knowledge discovery from new representations will be introduced and applications to different problems will be demonstrated.
Dr. Xiangliang Zhang is an Assistant Professor of Computer Science and directs the Machine Intelligence and kNowledge Engineering (http://mine.kaust.edu.sa) group at KAUST. Prior to joining KAUST, she was a European ERCIM research fellow in Norwegian University of Science and Technology (NTNU), Norway, in 2010. She earned her Ph.D. degree in computer science from INRIA-Universite Paris-Sud, France, in July 2010. She received M.S. and B.S. degrees from Xi’an Jiaotong University, China, in 2006 and 2003, respectively. Dr. Zhang's research mainly focuses on learning from complex and large-scale streaming data. Dr. Zhang has published over 70 research papers in referred international journals and conference proceedings, including TKDE, SIGKDD, VLDB J, AAAI, IJCAI, ICDM, ECML/PKDD, CIKM, InfoCom etc. She is the reviewer of TKDE, TKDD, VLDB J, Information Science, DMKD and KAIS. She serves on the Program Committee for premier conferences like SIGKDD, ICDM, AAAI, IJCAI etc.
For more info contact: Prof. Xiangliang Zhang: email: email@example.com
Date: Tuesday 21st Feb 2017
Time:03:00 PM - 04:00 PM
Location: Location: Building 9, Hall I Room 2322
Refreshments: will be available at 14:45 PM